Objective: Injuries are responsible for 11% of global disability-adjusted life years. Hearing difficulty may be a modifiable risk factor for injury. The primary aim was to determine whether subjective hearing difficulty is associated with increased incidence of injuries (all-type, workplace, and nonworkplace) serious enough to require health care among Americans aged 18 years and older. The secondary aim was to determine whether hearing difficulty is associated with increased use of health care for injuries. Design: The analysis was performed using data from the 1999 to 2012 Medical Expenditure Panel Surveys–Household Component (MEPS-HC). Multivariable estimates are generalizable to the American noninstitutionalized adult population in 2006. Hearing difficulty was determined by computer-assisted personal interview. The primary outcome was number of separate injuries requiring health care (emergency department visits, hospital inpatient admissions, or medical office visits) during the follow-up period. Crude injury incidence rates were calculated with 95% confidence intervals (CIs). Multivariable negative binomial regression was performed to determine the relative incidence rate of injuries among the group reporting hearing difficulties relative to the group reporting no difficulty in hearing, adjusting for relevant confounders. Variance estimates were adjusted to take into account the complex sample design. Results were stratified by sex. Results: Of the 121,453 participants in MEPS-HC panels 6-16, 107,352 (88.4%) had complete data and were included in the analysis. The average follow-up was 1.27 years. Seven thousand six hundred and twenty-four participants (7.1%) reported hearing difficulty at baseline. During the follow-up period, 10,727 participants (10.0%) had one or more injuries. In multivariable analyses, hearing difficulty was significantly associated with increased incidence of all-type (males: incidence rate ratio [IRR] 1.31, 95% CI 1.18–1.45, p value
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Δευτέρα 8 Ιανουαρίου 2018
Hearing Difficulty Is Associated With Injuries Requiring Medical Care
Objective: Injuries are responsible for 11% of global disability-adjusted life years. Hearing difficulty may be a modifiable risk factor for injury. The primary aim was to determine whether subjective hearing difficulty is associated with increased incidence of injuries (all-type, workplace, and nonworkplace) serious enough to require health care among Americans aged 18 years and older. The secondary aim was to determine whether hearing difficulty is associated with increased use of health care for injuries. Design: The analysis was performed using data from the 1999 to 2012 Medical Expenditure Panel Surveys–Household Component (MEPS-HC). Multivariable estimates are generalizable to the American noninstitutionalized adult population in 2006. Hearing difficulty was determined by computer-assisted personal interview. The primary outcome was number of separate injuries requiring health care (emergency department visits, hospital inpatient admissions, or medical office visits) during the follow-up period. Crude injury incidence rates were calculated with 95% confidence intervals (CIs). Multivariable negative binomial regression was performed to determine the relative incidence rate of injuries among the group reporting hearing difficulties relative to the group reporting no difficulty in hearing, adjusting for relevant confounders. Variance estimates were adjusted to take into account the complex sample design. Results were stratified by sex. Results: Of the 121,453 participants in MEPS-HC panels 6-16, 107,352 (88.4%) had complete data and were included in the analysis. The average follow-up was 1.27 years. Seven thousand six hundred and twenty-four participants (7.1%) reported hearing difficulty at baseline. During the follow-up period, 10,727 participants (10.0%) had one or more injuries. In multivariable analyses, hearing difficulty was significantly associated with increased incidence of all-type (males: incidence rate ratio [IRR] 1.31, 95% CI 1.18–1.45, p value
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Hearing Difficulty Is Associated With Injuries Requiring Medical Care
Objective: Injuries are responsible for 11% of global disability-adjusted life years. Hearing difficulty may be a modifiable risk factor for injury. The primary aim was to determine whether subjective hearing difficulty is associated with increased incidence of injuries (all-type, workplace, and nonworkplace) serious enough to require health care among Americans aged 18 years and older. The secondary aim was to determine whether hearing difficulty is associated with increased use of health care for injuries. Design: The analysis was performed using data from the 1999 to 2012 Medical Expenditure Panel Surveys–Household Component (MEPS-HC). Multivariable estimates are generalizable to the American noninstitutionalized adult population in 2006. Hearing difficulty was determined by computer-assisted personal interview. The primary outcome was number of separate injuries requiring health care (emergency department visits, hospital inpatient admissions, or medical office visits) during the follow-up period. Crude injury incidence rates were calculated with 95% confidence intervals (CIs). Multivariable negative binomial regression was performed to determine the relative incidence rate of injuries among the group reporting hearing difficulties relative to the group reporting no difficulty in hearing, adjusting for relevant confounders. Variance estimates were adjusted to take into account the complex sample design. Results were stratified by sex. Results: Of the 121,453 participants in MEPS-HC panels 6-16, 107,352 (88.4%) had complete data and were included in the analysis. The average follow-up was 1.27 years. Seven thousand six hundred and twenty-four participants (7.1%) reported hearing difficulty at baseline. During the follow-up period, 10,727 participants (10.0%) had one or more injuries. In multivariable analyses, hearing difficulty was significantly associated with increased incidence of all-type (males: incidence rate ratio [IRR] 1.31, 95% CI 1.18–1.45, p value
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All you should know about Waardenburg syndrome
A look at Waardenburg syndrome, a group of diseases that cause changes in the body and eye color. Find out about how rare it is and the diagnosis.
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All you should know about Waardenburg syndrome
A look at Waardenburg syndrome, a group of diseases that cause changes in the body and eye color. Find out about how rare it is and the diagnosis.
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All you should know about Waardenburg syndrome
A look at Waardenburg syndrome, a group of diseases that cause changes in the body and eye color. Find out about how rare it is and the diagnosis.
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Shocking News about Tinnitus Treatment
Precisely timed sound stimulation coupled with weak electrical pulses delivered to the neck or cheeks could bring relief from tinnitus. This is the potentially ground-breaking news from researchers at the University of Michigan. Let’s start at the beginning, shall we?
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Calibration of raw accelerometer data to measure physical activity: A systematic review
Publication date: March 2018
Source:Gait & Posture, Volume 61
Author(s): Márcio de Almeida Mendes, Inácio C.M. da Silva, Virgílio V. Ramires, Felipe F. Reichert, Rafaela C. Martins, Elaine Tomasi
Most of calibration studies based on accelerometry were developed using count-based analyses. In contrast, calibration studies based on raw acceleration signals are relatively recent and their evidences are incipient. The aim of the current study was to systematically review the literature in order to summarize methodological characteristics and results from raw data calibration studies. The review was conducted up to May 2017 using four databases: PubMed, Scopus, SPORTDiscus and Web of Science. Methodological quality of the included studies was evaluated using the Landis and Koch’s guidelines. Initially, 1669 titles were identified and, after assessing titles, abstracts and full-articles, 20 studies were included. All studies were conducted in high-income countries, most of them with relatively small samples and specific population groups. Physical activity protocols were different among studies and the indirect calorimetry was the criterion measure mostly used. High mean values of sensitivity, specificity and accuracy from the intensity thresholds of cut-point-based studies were observed (93.7%, 91.9% and 95.8%, respectively). The most frequent statistical approach applied was machine learning-based modelling, in which the mean coefficient of determination was 0.70 to predict physical activity energy expenditure. Regarding the recognition of physical activity types, the mean values of accuracy for sedentary, household and locomotive activities were 82.9%, 55.4% and 89.7%, respectively. In conclusion, considering the construct of physical activity that each approach assesses, linear regression, machine-learning and cut-point-based approaches presented promising validity parameters.
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Source:Gait & Posture, Volume 61
Author(s): Márcio de Almeida Mendes, Inácio C.M. da Silva, Virgílio V. Ramires, Felipe F. Reichert, Rafaela C. Martins, Elaine Tomasi
Most of calibration studies based on accelerometry were developed using count-based analyses. In contrast, calibration studies based on raw acceleration signals are relatively recent and their evidences are incipient. The aim of the current study was to systematically review the literature in order to summarize methodological characteristics and results from raw data calibration studies. The review was conducted up to May 2017 using four databases: PubMed, Scopus, SPORTDiscus and Web of Science. Methodological quality of the included studies was evaluated using the Landis and Koch’s guidelines. Initially, 1669 titles were identified and, after assessing titles, abstracts and full-articles, 20 studies were included. All studies were conducted in high-income countries, most of them with relatively small samples and specific population groups. Physical activity protocols were different among studies and the indirect calorimetry was the criterion measure mostly used. High mean values of sensitivity, specificity and accuracy from the intensity thresholds of cut-point-based studies were observed (93.7%, 91.9% and 95.8%, respectively). The most frequent statistical approach applied was machine learning-based modelling, in which the mean coefficient of determination was 0.70 to predict physical activity energy expenditure. Regarding the recognition of physical activity types, the mean values of accuracy for sedentary, household and locomotive activities were 82.9%, 55.4% and 89.7%, respectively. In conclusion, considering the construct of physical activity that each approach assesses, linear regression, machine-learning and cut-point-based approaches presented promising validity parameters.
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Calibration of raw accelerometer data to measure physical activity: A systematic review
Publication date: March 2018
Source:Gait & Posture, Volume 61
Author(s): Márcio de Almeida Mendes, Inácio C.M. da Silva, Virgílio V. Ramires, Felipe F. Reichert, Rafaela C. Martins, Elaine Tomasi
Most of calibration studies based on accelerometry were developed using count-based analyses. In contrast, calibration studies based on raw acceleration signals are relatively recent and their evidences are incipient. The aim of the current study was to systematically review the literature in order to summarize methodological characteristics and results from raw data calibration studies. The review was conducted up to May 2017 using four databases: PubMed, Scopus, SPORTDiscus and Web of Science. Methodological quality of the included studies was evaluated using the Landis and Koch’s guidelines. Initially, 1669 titles were identified and, after assessing titles, abstracts and full-articles, 20 studies were included. All studies were conducted in high-income countries, most of them with relatively small samples and specific population groups. Physical activity protocols were different among studies and the indirect calorimetry was the criterion measure mostly used. High mean values of sensitivity, specificity and accuracy from the intensity thresholds of cut-point-based studies were observed (93.7%, 91.9% and 95.8%, respectively). The most frequent statistical approach applied was machine learning-based modelling, in which the mean coefficient of determination was 0.70 to predict physical activity energy expenditure. Regarding the recognition of physical activity types, the mean values of accuracy for sedentary, household and locomotive activities were 82.9%, 55.4% and 89.7%, respectively. In conclusion, considering the construct of physical activity that each approach assesses, linear regression, machine-learning and cut-point-based approaches presented promising validity parameters.
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Source:Gait & Posture, Volume 61
Author(s): Márcio de Almeida Mendes, Inácio C.M. da Silva, Virgílio V. Ramires, Felipe F. Reichert, Rafaela C. Martins, Elaine Tomasi
Most of calibration studies based on accelerometry were developed using count-based analyses. In contrast, calibration studies based on raw acceleration signals are relatively recent and their evidences are incipient. The aim of the current study was to systematically review the literature in order to summarize methodological characteristics and results from raw data calibration studies. The review was conducted up to May 2017 using four databases: PubMed, Scopus, SPORTDiscus and Web of Science. Methodological quality of the included studies was evaluated using the Landis and Koch’s guidelines. Initially, 1669 titles were identified and, after assessing titles, abstracts and full-articles, 20 studies were included. All studies were conducted in high-income countries, most of them with relatively small samples and specific population groups. Physical activity protocols were different among studies and the indirect calorimetry was the criterion measure mostly used. High mean values of sensitivity, specificity and accuracy from the intensity thresholds of cut-point-based studies were observed (93.7%, 91.9% and 95.8%, respectively). The most frequent statistical approach applied was machine learning-based modelling, in which the mean coefficient of determination was 0.70 to predict physical activity energy expenditure. Regarding the recognition of physical activity types, the mean values of accuracy for sedentary, household and locomotive activities were 82.9%, 55.4% and 89.7%, respectively. In conclusion, considering the construct of physical activity that each approach assesses, linear regression, machine-learning and cut-point-based approaches presented promising validity parameters.
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Calibration of raw accelerometer data to measure physical activity: A systematic review
Publication date: March 2018
Source:Gait & Posture, Volume 61
Author(s): Márcio de Almeida Mendes, Inácio C.M. da Silva, Virgílio V. Ramires, Felipe F. Reichert, Rafaela C. Martins, Elaine Tomasi
Most of calibration studies based on accelerometry were developed using count-based analyses. In contrast, calibration studies based on raw acceleration signals are relatively recent and their evidences are incipient. The aim of the current study was to systematically review the literature in order to summarize methodological characteristics and results from raw data calibration studies. The review was conducted up to May 2017 using four databases: PubMed, Scopus, SPORTDiscus and Web of Science. Methodological quality of the included studies was evaluated using the Landis and Koch’s guidelines. Initially, 1669 titles were identified and, after assessing titles, abstracts and full-articles, 20 studies were included. All studies were conducted in high-income countries, most of them with relatively small samples and specific population groups. Physical activity protocols were different among studies and the indirect calorimetry was the criterion measure mostly used. High mean values of sensitivity, specificity and accuracy from the intensity thresholds of cut-point-based studies were observed (93.7%, 91.9% and 95.8%, respectively). The most frequent statistical approach applied was machine learning-based modelling, in which the mean coefficient of determination was 0.70 to predict physical activity energy expenditure. Regarding the recognition of physical activity types, the mean values of accuracy for sedentary, household and locomotive activities were 82.9%, 55.4% and 89.7%, respectively. In conclusion, considering the construct of physical activity that each approach assesses, linear regression, machine-learning and cut-point-based approaches presented promising validity parameters.
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Source:Gait & Posture, Volume 61
Author(s): Márcio de Almeida Mendes, Inácio C.M. da Silva, Virgílio V. Ramires, Felipe F. Reichert, Rafaela C. Martins, Elaine Tomasi
Most of calibration studies based on accelerometry were developed using count-based analyses. In contrast, calibration studies based on raw acceleration signals are relatively recent and their evidences are incipient. The aim of the current study was to systematically review the literature in order to summarize methodological characteristics and results from raw data calibration studies. The review was conducted up to May 2017 using four databases: PubMed, Scopus, SPORTDiscus and Web of Science. Methodological quality of the included studies was evaluated using the Landis and Koch’s guidelines. Initially, 1669 titles were identified and, after assessing titles, abstracts and full-articles, 20 studies were included. All studies were conducted in high-income countries, most of them with relatively small samples and specific population groups. Physical activity protocols were different among studies and the indirect calorimetry was the criterion measure mostly used. High mean values of sensitivity, specificity and accuracy from the intensity thresholds of cut-point-based studies were observed (93.7%, 91.9% and 95.8%, respectively). The most frequent statistical approach applied was machine learning-based modelling, in which the mean coefficient of determination was 0.70 to predict physical activity energy expenditure. Regarding the recognition of physical activity types, the mean values of accuracy for sedentary, household and locomotive activities were 82.9%, 55.4% and 89.7%, respectively. In conclusion, considering the construct of physical activity that each approach assesses, linear regression, machine-learning and cut-point-based approaches presented promising validity parameters.
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Voice Discrimination by Adults with Cochlear Implants: the Benefits of Early Implantation for Vocal-Tract Length Perception
Abstract
Cochlear implant (CI) users find it extremely difficult to discriminate between talkers, which may partially explain why they struggle to understand speech in a multi-talker environment. Recent studies, based on findings with postlingually deafened CI users, suggest that these difficulties may stem from their limited use of vocal-tract length (VTL) cues due to the degraded spectral resolution transmitted by the CI device. The aim of the present study was to assess the ability of adult CI users who had no prior acoustic experience, i.e., prelingually deafened adults, to discriminate between resynthesized “talkers” based on either fundamental frequency (F0) cues, VTL cues, or both. Performance was compared to individuals with normal hearing (NH), listening either to degraded stimuli, using a noise-excited channel vocoder, or non-degraded stimuli. Results show that (a) age of implantation was associated with VTL but not F0 cues in discriminating between talkers, with improved discrimination for those subjects who were implanted at earlier age; (b) there was a positive relationship for the CI users between VTL discrimination and speech recognition score in quiet and in noise, but not with frequency discrimination or cognitive abilities; (c) early-implanted CI users showed similar voice discrimination ability as the NH adults who listened to vocoded stimuli. These data support the notion that voice discrimination is limited by the speech processing of the CI device. However, they also suggest that early implantation may facilitate sensory-driven tonotopicity and/or improve higher-order auditory functions, enabling better perception of VTL spectral cues for voice discrimination.
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